1. Field of Invention
The present invention relates to an automated computer based apparatus and method for making product recommendations over an electronic commerce network. More particularly, the invention is directed to an apparatus and method for making product recommendations based on customer user behavior.
2. Discussion of Related Art
With the recent increase in popularity of on-line shopping over the Internet, entities providing the shopping sites are interested in obtaining information on shoppers that would help in selling their products. The traditional customer or market surveys used in obtaining such information are applicable and usable for the providers. Companies such as Likeminds, Inc. (www.likeminds.com) and Firefly Network, Inc. (www.firefly.com) provide survey information which are based on explicit ratings by customers, commonly referred to in the art as recommendation engines using xe2x80x98collaborative filteringxe2x80x99. The use of engines based on ratings have particular applicability in products which are uniform and of a particular type. For example, in the case of Likeminds, customers are asked to provide ratings for preferences for products such as compact discs, based on their degrees of likes or dislikes. These ratings are then collected and archived for later use. At some point in the future a product recommendation will be made to a new customer based on the previously archived data of other customers. Recommendations made based on past explicit ratings by customers are known as xe2x80x98collaborative filteringxe2x80x99.
The collaborative filtering approach does not work well if the customers do not participate in the explicit ratings of products. A customer or purchaser in an e-commerce environment typically prefers to minimize his time on-line and is usually unwilling to spend extra time, especially in rating products, on-line or otherwise.
Other companies offer xe2x80x98content-basedxe2x80x99 filtering which uses extracted texts and information from e-commerce websites.
An example is the intelligent infrastructure offered by Autonomy, Inc. (www.autonomy.com) . This system provides an Agentware content server, which is a scaleable content personalization and organization engine for Internet information providers. This technique extracts key concepts from documents and websites to automate the categorization, cross-referencing, hyperlinking, and presentation of the information. The customer profiling system of this software enables information and service providers to understand the interests of customers and deliver personalized information.
Another company which provides intelligent servers is Aptex Software (www.aptex.com). Aptex uses a xe2x80x98Content Miningxe2x80x99 method which automatically analyzes text and other unstructured content to make intelligent decisions and recommendations.
Net Perceptions is still another company (www.netperceptions.com) which uses (Grouplens) implicit or explicit ratings of products to provide recommendations. Implicit ratings refer to the set of products bought or browsed by a customer.
Despite the provision and availability of the above described intelligent servers, a need still exists for a method or system for facilitating characterization of customers and products on the basis of customers"" natural browsing/purchasing behavior, without resorting to explicit group product ratings, and providing recommendations based on peer group categorization, affording substantial customer personalization to the product recommendation process.
It is therefore an object of the present invention to provide a system and a method of using characterizations of products and user behavior, including user browsing or purchasing behavior, to generate product recommendations at an e-commerce site.
It is another object of the present invention to utilize the characterizations to create peer groups to personalize the recommendation to users. A peer group is a collection of customers whose product preferences have been previously archived and whom display a pattern of product preferences similar to that of the new customer.
The above objectives are accomplished by a method according to the present invention, which provides product recommendations to customers in an e-commerce environment, comprising the steps of: deriving product characterizations from each of a plurality of products; creating individual customer characterizations on each of the customers based on usage of the product characterizations by each of the respective customers; clustering based on similarities in the customer characterizations, to form peer groups; categorizing individual customers into one of the peer groups; and making product recommendations to customers based on the customer characterizations and the categorized peer groups.
The step of creating customer characterizations preferably includes extracting product characterizations when the customers browse or purchase the products, and the step of creating customer characterizations may include concatenating each of the product characterizations of all products browsed or purchased by an individual customer.
Preferably, the product characterizations are derived from text characterizations of each of the products and may further include the steps of: finding the frequency of occurrence for each word in the text descriptions; dividing the total frequency for each word by the frequency of occurrence for the word for all customers; finding the standard deviation for each word; selecting words having larger standard deviations; and expressing a product characterization based on the selected word.
Another method of the invention preferably include the steps of: (a) deriving product characterizations based on a current on-line session; (b) accessing historical product characterizations from memory; (c) creating a customer characterization by weighted concatenating characterizations from steps (a) and (b); (d) computing a cluster centroid for each of the peer groups; (e) selecting the peer groups whose cluster centroid is closest to the characterization created in step (c); and (f) generating one of product, peer and profile recommendations based on the selected peer group. The cluster centroid may be computed by concatenating text characterizations of all customer characterizations in each peer group, and the recommendations may comprise a weighted concatenation of text-characterizations of products bought and browsed in the current on-line session.
A system according to the present invention provides, a computer having a processor and stored program for causing the computer to interact with customers in an e-commerce environment and to provide answers to customer inquiries, the stored program comprises: means for deriving product characterizations for a plurality of products, means for creating customer characterizations based on usage of the product characterizations by the customers, means for clustering, based on similarities in the customer characterizations, for forming a plurality of peer groups, means for placing individual customers into one of the peer groups, and means for providing answers to the customer queries based on the customer characterizations and information from the peer groups.
The system preferably includes storage for archiving the customer characterizations.
The stored program further includes means for creating present customer characterizations for a currently on-line customer based on a concatenation of data extracted from usage of product characterizations by the currently on-line customer.
The stored program further includes means for placing a currently on-line customer in one of the peer groups based on similarities in the present customer characterizations and stored characterizations.
The means for deriving product characterizations preferably includes means for extracting text descriptions associated with the products.
The means for providing answers preferably includes means for recommending products to an individual customer based on product characterizations from products browsed by the individual customer in a current session.
The means for providing answers preferably includes means for recommending products to an individual customer based on a weighted frequency of historic product characterizations and a weighted frequency of present product characterizations from products browsed by the individual customer in a current session.
The means for providing answers also preferably includes means for recommending products to an individual customer based on historic product characterizations.